State-of-the-art approaches in instance segmentation often follow the Mask R-CNN [1]} paradigm with the first stage detecting bounding boxes, followed by the second stage to segment instance masks. Mask R-CNN and its variants [2]}, [3]}, [4]}, [5]}, [6]} have demonstrated notable performance, and most of the leading approaches in the COCO instance segmentation challenge [7]} have adopted this pipeline. However, we note that most incremental improvement comes from better backbone architecture designs, with little attention paid in the instance mask regression after obtaining the ROI (Region-of-Interest) features from object detection. We observe that a lot of segmentation errors are caused by overlapping objects, especially for object instances belonging to the same class. This is because each instance mask is individually regressed, and the regression process implicitly assumes the object in an ROI has almost complete contour, since most objects in the training data in COCO do not exhibit significant occlusions.
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